Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,52 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import pandas as pd
+import joblib
+import numpy as np
+import os
+
+# Load model and scaler
+MODEL_PATH = os.path.join(os.path.dirname(__file__), "model", "xgb_model.joblib")
+SCALER_PATH = os.path.join(os.path.dirname(__file__), "model", "scaler.joblib")
+
+model = joblib.load(MODEL_PATH)
+scaler = joblib.load(SCALER_PATH)
+
+st.title("Hemoglobin Level Predictor")
+
+st.markdown(
+    """
+    ### Developed by Dr. Vinod Kumar Yata's research group  
+    School of Allied and Healthcare Sciences, Malla Reddy University, Hyderabad, India
+
+    ---
+    ⚠️ **Warning**:  
+    This is an experimental tool and should not be used for medical diagnosis.  
+    Always consult a licensed healthcare provider for medical advice.
+    ---
+    """
+)
+
+# Input fields
+age = st.number_input("Age", min_value=0, max_value=120, value=30)
+gender = st.selectbox("Gender", options=["Male", "Female"])
+o2_saturation = st.slider("O2 Saturation (%)", min_value=50.0, max_value=100.0, value=98.0)
+bp_systolic = st.number_input("Systolic BP", min_value=50, max_value=200, value=120)
+bp_diastolic = st.number_input("Diastolic BP", min_value=30, max_value=130, value=80)
+respiratory_rate = st.number_input("Respiratory Rate (breaths/min)", min_value=5, max_value=60, value=18)
+
+# Map gender to numeric
+gender_num = 1 if gender == "Male" else 0
+
+input_df = pd.DataFrame([{
+    "Age": age,
+    "Gender": gender_num,
+    "O2_Saturation": o2_saturation,
+    "BP_Systolic": bp_systolic,
+    "BP_Diastolic": bp_diastolic,
+    "Respiratory_Rate": respiratory_rate
+}])
+
+if st.button("Predict Hemoglobin Level"):
+    input_scaled = scaler.transform(input_df)
+    prediction = model.predict(input_scaled)[0]
+    st.success(f"Predicted Hemoglobin Level: {prediction:.2f} g/dL")